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True negative (TN): Difference between revisions
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(Created page with "===Introduction== Machine learning classification is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: true positive (TP), true negative (TN), false positive (FP) and false negative (FN). In this article, we'll focus on the true negative (TN) outcome. ==What is True Negative (TN)?== True Negative (TN) is one of the possible outcomes in a binary classification problem when the model predic...") |
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==Introduction== | |||
Machine learning classification is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: true positive (TP), true negative (TN), false positive (FP) and false negative (FN) | [[True negative (TN)]] is when the [[machine learning model]] correctly predicts the [[negative class]]. [[Machine learning]] [[classification]] is the process of accurately predicting a data point's class based on features. This classification can lead to four distinct outcomes: [[true positive (TP)]], [[true negative (TN)]], [[false positive (FP)]] and [[false negative (FN)]]. | ||
==What is True Negative (TN)?== | ==What is True Negative (TN)?== | ||